Use of deep learning to predict chronic wasting disease status based on animal movement
Raymond L. Blaha, Christopher J. Silva, Stephanie A. Cunningham, Melia T. DeVivo, David R. Edmunds, Melanie R. Boudreau · 2026
Read Use of deep learning to predict chronic wasting disease status based on animal movement by Raymond L. Blaha, Christopher J. Silva, Stephanie A. Cunningham, Melia T. DeVivo, David R. Edmunds, Melanie R. Boudreau (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/PMC13479571.txt.
Description
Raymond L. Blaha and colleagues published a 2026 study using deep learning on deer movement data to flag chronic wasting disease, a fatal prion illness of deer and elk. Infected animals can look healthy for months, so behavior change might betray infection before symptoms. GPS collar records from free-ranging mule deer provided the raw material.
Unsupervised autoencoder models, trained to recognize normal movement, spotted anomalous animals with at least 84 percent accuracy and 89 percent precision. Speed, direction, and path winding measures mattered most in separating infected from healthy deer. A supervised variant performed differently, a contrast the authors examine. Wildlife managers could use such screening to focus testing and culling where collars already track herds.
Catalog record
| CREATOR | Raymond L. Blaha, Christopher J. Silva, Stephanie A. Cunningham, Melia T. DeVivo, David R. Edmunds, Melanie R. Boudreau |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 61665 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |